将多模态特征融合引入联邦推荐，提升隐私保护下的推荐效果。
Multimodal-enhanced Federated Recommendation: A Group-wise Fusion Approach
- 服务器端统一处理多模态表示，降低客户端负担。
- 分组融合机制实现用户间细粒度知识共享，保留个性化偏好。
- 可无缝集成现有系统，适合需隐私保护的推荐场景。
联邦推荐（FR）是一种在保护隐私的前提下解决排序学习问题的新范式。如何在效率、分布异构性和细粒度对齐方面有效整合多模态特征，仍是开放挑战。为此，我们提出一种新型的联邦推荐多模态融合机制（GFMFR）。具体而言，该方法将多模态表征学习任务卸载至服务器端，服务器存储物品内容并使用高容量编码器生成丰富表征，从而减轻客户端开销。此外，一种面向分组的物品表征融合方法，使相似用户间能进行细粒度知识共享，同时保留个体偏好。所提出的融合损失可直接嵌入任意现有联邦推荐系统，增强其多模态特征能力。在五个公开基准数据集上的大量实验表明，GFMFR 均显著优于当前最优的多模态联邦推荐基线。
原文摘要 · Abstract (English)
Federated Recommendation (FR) is a new learning paradigm to tackle the learn-to-rank problem in a privacy-preservation manner. How to integrate multi-modality features into federated recommendation is still an open challenge in terms of efficiency, distribution heterogeneity, and fine-grained alignment. To address these challenges, we propose a novel multimodal fusion mechanism in federated recommendation settings (GFMFR). Specifically, it offloads multimodal representation learning to the server, which stores item content and employs a high-capacity encoder to generate expressive representations, alleviating client-side overhead. Moreover, a group-aware item representation fusion approach enables fine-grained knowledge sharing among similar users while retaining individual preferences. The proposed fusion loss could be simply plugged into any existing federated recommender systems empowering their capability by adding multi-modality features. Extensive experiments on five public benchmark datasets demonstrate that GFMFR consistently outperforms state-of-the-art multimodal FR baselines.
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